| 2014 NOAA Post-Sandy Topobathymetric LiDAR: Void DEMs South Carolina to New York | identificationInfo|
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| (MI_Metadata) fileIdentifier: gov.noaa.nmfs.inport:48367 language: LanguageCode: eng characterSet: (MD_CharacterSetCode) UTF8 hierarchyLevel: (MD_ScopeCode) dataset contact: (CI_ResponsibleParty) organisationName: Office for Coastal Management contactInfo: (CI_Contact) phone: (CI_Telephone) voice: (missing) address: (CI_Address) role: (CI_RoleCode) resourceProvider contact: (CI_ResponsibleParty) organisationName: NOAA Office for Coastal Management contactInfo: (CI_Contact) phone: (CI_Telephone) voice: (843) 740-1202 address: (CI_Address) deliveryPoint: 2234 South Hobson Ave city: Charleston administrativeArea: SC postalCode: 29405-2413 country: (missing) electronicMailAddress: coastal.info@noaa.gov onlineResource: (CI_OnlineResource) linkage: https://coast.noaa.gov protocol: WWW:LINK-1.0-http--link name: NOAA Office for Coastal Management Website description: NOAA Office for Coastal Management Home Page function: (CI_OnLineFunctionCode) information role: (CI_RoleCode) pointOfContact dateStamp: DateTime: 2024-01-10T18:52:45 metadataStandardName: ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for imagery and gridded data metadataStandardVersion: ISO 19115-2:2009(E) return to top identificationInfo: (MD_DataIdentification) citation: (CI_Citation) title: 2014 NOAA Post-Sandy Topobathymetric LiDAR: Void DEMs South Carolina to New York alternateTitle: 2014_NOAA_postSandy_DEM_m4967_metadata date: (CI_Date) date: 2015-12-20 dateType: (CI_DateTypeCode) publication identifier: (MD_Identifier) authority: (CI_Citation) title: NOAA/NMFS/EDM date: (inapplicable) code: Anchor: InPort Catalog ID 48367 citedResponsibleParty: (CI_ResponsibleParty) organisationName: (inapplicable) contactInfo: (CI_Contact) onlineResource: (CI_OnlineResource) linkage: https://www.fisheries.noaa.gov/inport/item/48367 protocol: WWW:LINK-1.0-http--link name: Full Metadata Record description: View the complete metadata record on InPort for more information about this dataset. function: (CI_OnLineFunctionCode) information role: (inapplicable) citedResponsibleParty: (CI_ResponsibleParty) organisationName: (inapplicable) contactInfo: (CI_Contact) onlineResource: (CI_OnlineResource) linkage: https://coast.noaa.gov/dataviewer protocol: WWW:LINK-1.0-http--link name: Citation URL description: Online Resource function: (CI_OnLineFunctionCode) download role: (inapplicable) presentationForm: (CI_PresentationFormCode) mapDigital abstract: These data were collected by the National Oceanic Atmospheric Administration National Geodetic Survey Remote Sensing Division using a Riegl VQ820G system. The data were acquired from 201311- 201406. The data includes topobathy data with points classified by target type (e.g. ground, water, etc). The final classified LiDAR data were then used to create topobathymetric DEMs in IMG format with 1m pixel size using ground points. The full project consists of 2,775 square miles along the Atlantic Coast from New York to South Carolina, or 41,388 - 500 m x 500 m lidar tiles. These tiles have been combined into 140 larger blocks. The data collection and processing was funded by post-Sandy supplemental funds. While Sandy was considered an extra-tropical storm when it struck, the word hurricane is in this sentence for search purposes. Original contact information: Contact Org: National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Geodetic Survey (NGS), Remote Sensing Division Title: Chief, Remote Sensing Division Phone: 301-713-2663 purpose: This lidar data (and digital camera imagery collected under the same task order) was required by the National Geodetic Survey (NGS), Remote Sensing Division Coastal Mapping Program (CMP) to enable accurate and consistent measurement of the national shoreline. The CMP works to provide a regularly updated and consistent national shoreline to define America's marine territorial limits and manage coastal resources. credit: We request that you credit the National Oceanic and Atmospheric Administration (NOAA) when you use these data in a report, publication, or presentation. status: (MD_ProgressCode) completed pointOfContact: (CI_ResponsibleParty) organisationName: NOAA Office for Coastal Management contactInfo: (CI_Contact) phone: (CI_Telephone) voice: (843) 740-1202 address: (CI_Address) deliveryPoint: 2234 South Hobson Ave city: Charleston administrativeArea: SC postalCode: 29405-2413 country: (missing) electronicMailAddress: coastal.info@noaa.gov onlineResource: (CI_OnlineResource) linkage: https://coast.noaa.gov protocol: WWW:LINK-1.0-http--link name: NOAA Office for Coastal Management Website description: NOAA Office for Coastal Management Home Page function: (CI_OnLineFunctionCode) information role: (CI_RoleCode) pointOfContact pointOfContact: (CI_ResponsibleParty) organisationName: NOAA Office for Coastal Management contactInfo: (CI_Contact) phone: (CI_Telephone) voice: (843) 740-1202 address: (CI_Address) deliveryPoint: 2234 South Hobson Ave city: Charleston administrativeArea: SC postalCode: 29405-2413 country: (missing) electronicMailAddress: coastal.info@noaa.gov onlineResource: (CI_OnlineResource) linkage: https://coast.noaa.gov protocol: WWW:LINK-1.0-http--link name: NOAA Office for Coastal Management Website description: NOAA Office for Coastal Management Home Page function: (CI_OnLineFunctionCode) information role: (CI_RoleCode) custodian resourceMaintenance: (MD_MaintenanceInformation) maintenanceAndUpdateFrequency: (MD_MaintenanceFrequencyCode) notPlanned descriptiveKeywords: (MD_Keywords) keyword: EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION > TOPOGRAPHICAL RELIEF MAPS keyword: EARTH SCIENCE > OCEANS > COASTAL PROCESSES > COASTAL ELEVATION type: (MD_KeywordTypeCode) theme thesaurusName: (CI_Citation) title: Global Change Master Directory (GCMD) Science Keywords date: (missing) edition: 17.0 descriptiveKeywords: (MD_Keywords) keyword: CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > DELAWARE keyword: CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > MARYLAND keyword: CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > NEW JERSEY keyword: CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > NEW YORK keyword: CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > NORTH CAROLINA keyword: CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > SOUTH CAROLINA keyword: CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > VIRGINIA type: (MD_KeywordTypeCode) place thesaurusName: (CI_Citation) title: Global Change Master Directory (GCMD) Location Keywords date: (missing) edition: 17.0 descriptiveKeywords: (MD_Keywords) keyword: DEM type: (MD_KeywordTypeCode) theme descriptiveKeywords: (MD_Keywords) keyword: 201311 keyword: 201406 type: (MD_KeywordTypeCode) temporal descriptiveKeywords: (MD_Keywords) keyword: DOC/NOAA/NOS/OCM > Office of Coastal Management, National Ocean Service, NOAA, U.S. Department of Commerce type: (MD_KeywordTypeCode) dataCentre thesaurusName: (CI_Citation) title: Global Change Master Directory (GCMD) Data Center Keywords date: (CI_Date) date: 2017-04-24 dateType: (CI_DateTypeCode) publication edition: 8.5 citedResponsibleParty: GCMD Landing Page descriptiveKeywords: (MD_Keywords) keyword: DEMs type: (MD_KeywordTypeCode) project thesaurusName: (CI_Citation) title: InPort date: (inapplicable) resourceConstraints: (MD_LegalConstraints) useConstraints: (MD_RestrictionCode) otherRestrictions otherConstraints: Cite As: Office for Coastal Management, [Date of Access]: 2014 NOAA Post-Sandy Topobathymetric LiDAR: Void DEMs South Carolina to New York [Data Date Range], https://www.fisheries.noaa.gov/inport/item/48367. resourceConstraints: (MD_Constraints) useLimitation: NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose. resourceConstraints: (MD_LegalConstraints) accessConstraints: (MD_RestrictionCode) otherRestrictions useConstraints: (MD_RestrictionCode) otherRestrictions otherConstraints: Access Constraints: None | Use Constraints: Users should be aware that temporal changes may have occurred since this data set was collected and some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations. | Distribution Liability: Any conclusions drawn from the analysis of this information are not the responsibility of NOAA, the National Geodetic Survey, the Office for Coastal Management, or its partners. resourceConstraints: (MD_SecurityConstraints) classification: (MD_ClassificationCode) unclassified classificationSystem: (missing) handlingDescription: (missing) aggregationInfo: (MD_AggregateInformation) aggregateDataSetName: (CI_Citation) title: NOAA Data Management Plan (DMP) date: (unknown) identifier: (MD_Identifier) authority: (CI_Citation) title: NOAA/NMFS/EDM date: (inapplicable) code: 48367 citedResponsibleParty: (CI_ResponsibleParty) organisationName: (inapplicable) contactInfo: (CI_Contact) onlineResource: (CI_OnlineResource) linkage: https://www.fisheries.noaa.gov/inportserve/waf/noaa/nos/ocm/dmp/pdf/48367.pdf protocol: WWW:LINK-1.0-http--link name: NOAA Data Management Plan (DMP) description: NOAA Data Management Plan for this record on InPort. function: (CI_OnLineFunctionCode) information role: (inapplicable) associationType: (DS_AssociationTypeCode) crossReference spatialRepresentationType: (MD_SpatialRepresentationTypeCode) grid language: eng; US topicCategory: (MD_TopicCategoryCode) elevation environmentDescription: OS Independent extent: (EX_Extent) geographicElement: (EX_GeographicBoundingBox) westBoundLongitude: -79.675197 eastBoundLongitude: -71.83883 southBoundLatitude: 33.17856 northBoundLatitude: 41.13731 temporalElement: (EX_TemporalExtent) extent: TimePeriod: description: | Currentness: Ground Condition beginPosition: 2013-11-01 endPosition: 2014-06-01 supplementalInformation: Data include all lidar returns. An automated grounding classification algorithm was used to determine bare earth and submerged topography point classification. The automated grounding was followed with manual editing. Classes 2 (ground) and 26 (submerged topography) were used to create the final DEMs. The full workflow used for this project is found in the Supplemental Sandy Topobathymetric Processing and QC documentation. return to top distributionInfo: (MD_Distribution) distributor: (MD_Distributor) distributorContact: (CI_ResponsibleParty) organisationName: NOAA Office for Coastal Management contactInfo: (CI_Contact) phone: (CI_Telephone) voice: (843) 740-1202 address: (CI_Address) deliveryPoint: 2234 South Hobson Ave city: Charleston administrativeArea: SC postalCode: 29405-2413 country: (missing) electronicMailAddress: coastal.info@noaa.gov onlineResource: (CI_OnlineResource) linkage: https://coast.noaa.gov protocol: WWW:LINK-1.0-http--link name: NOAA Office for Coastal Management Website description: NOAA Office for Coastal Management Home Page function: (CI_OnLineFunctionCode) information role: (CI_RoleCode) distributor transferOptions: (MD_DigitalTransferOptions) onLine: (CI_OnlineResource) linkage: https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=4967 protocol: WWW:LINK-1.0-http--link name: Customized Download description: Create custom data files by choosing data area, product type, map projection, file format, datum, etc. function: (CI_OnLineFunctionCode) download transferOptions: (MD_DigitalTransferOptions) onLine: (CI_OnlineResource) linkage: https://noaa-nos-coastal-lidar-pds.s3.us-east-1.amazonaws.com/dem/Post_Sandy_DEM_2014_4967/index.html protocol: WWW:LINK-1.0-http--link name: Bulk Download description: Simple download of data files. function: (CI_OnLineFunctionCode) download return to top dataQualityInfo: (DQ_DataQuality) scope: (DQ_Scope) level: (MD_ScopeCode) dataset report: (DQ_AbsoluteExternalPositionalAccuracy) nameOfMeasure: Horizontal Positional Accuracy evaluationMethodDescription: Project specifications require horizontal positions to meet 1.0m RMSE. Independent horizontal accuracy testing requires photo-identifiable survey checkpoints, which is not always possible with elevation data. Where survey checkpoints are identifiable on LiDAR intensity imagery, horizontal accuracy will be computed for the lidar data. The DEMs are derived from the source LiDAR and inherit the accuracy of the source data. The DEMs are created using controlled and tested methods to limit the amount of error introduced during DEM production. This elevation data is compiled to meet the 1.0 m RMSE horizontal accuracy specification through rigorous processing of airborne GPS and IMU, use of control, and calibration procedures. result: (missing) report: (DQ_AbsoluteExternalPositionalAccuracy) nameOfMeasure: Vertical Positional Accuracy evaluationMethodDescription: The DEMs are derived from the source LiDAR and inherit the accuracy of the source data. The DEMs are created using controlled and tested methods to limit the amount of error introduced during DEM production so that any differences identified between the source LiDAR and final DEMs can be attributed to interpolation differences. DEMs are created by averaging several LiDAR points within each pixel which may result in slightly different elevation values at a given location when compared to the source LAS, which does not average several LiDAR points together but may interpolate (linearly) between two or three points to derive an elevation value. The final vertical accuracy of the DEMs was tested by Dewberry with 313 independent survey checkpoints for the entire Post Sandy Topobathymetric LiDAR Mapping for Shoreline Mapping project area. The survey checkpoints are evenly distributed, as much as possible, throughout the project area in five land cover categories: bare earth, open terrain, and urban areas (62), tall weeds and crops (68), forested and fully grown (68), brush and small trees (63), and submerged topography-hard bottom (52). The vertical accuracy is tested by comparing survey checkpoints to the final topobathymetric DEM surface. Accuracyz Based on the vertical accuracy testing conducted by Dewberry, using NSSDA and FEMA methodology, vertical accuracy at the 95% confidence level (called Accuracyz) is computed by the formula RMSEz x 1.9600. The final dataset satisfies the criteria: DEM dataset tested 0.112 m vertical accuracy at 95% confidence level in open terrain, based on RMSEz (0.057 m) x 1.9600. Based on the vertical accuracy testing conducted by Dewberry, using NSSDA and FEMA methodology, vertical accuracy at the 95% confidence level (called Accuracyz) is computed by the formula RMSEz x 1.9600. The final dataset satisfies the criteria: DEM dataset tested 0.331 m vertical accuracy at 95% confidence level in submerged topography, based on RMSEz (0.169 m) x 1.9600. Consolidated Vertical Accuracy (CVA) Based on the vertical accuracy testing conducted by Dewberry, using NDEP and ASPRS methodology, consolidated vertical accuracy (CVA) is computed using the 95th percentile method. The final dataset satisfies the criteria: DEM dataset tested 0.215 m consolidated vertical accuracy at 95th percentile in all land cover categories combined, excluding submerged topography. Supplemental Vertical Accuracy (SVA) Based on the vertical accuracy testing conducted by Dewberry, using NDEP and ASPRS methodology, supplemental vertical accuracy (SVA) is computed using the 95th percentile method. The final dataset satisfies the criteria: DEM dataset tested 0.254 m supplemental vertical accuracy at 95th percentile in the brushlands and trees land cover category. Based on the vertical accuracy testing conducted by Dewberry, using NDEP and ASPRS methodology, supplemental vertical accuracy (SVA) is computed using the 95th percentile method. The dataset satisfies the criteria: DEM dataset tested 0.240 m supplemental vertical accuracy at 95th percentile in the tall weeds and crops land cover category. Based on the vertical accuracy testing conducted by Dewberry, using NDEP and ASPRS methodology, supplemental vertical accuracy (SVA) is computed using the 95th percentile method. The final dataset satisfies the criteria: DEM dataset tested 0.142 m supplemental vertical accuracy at 95th percentile in the forested and fully grown land cover category. result: (missing) report: (DQ_CompletenessCommission) nameOfMeasure: Completeness Report evaluationMethodDescription: Data covers the 41,388 project tiles (500m x 500m tiles) mosaiced into 140 blocks. result: (missing) report: (DQ_ConceptualConsistency) nameOfMeasure: Conceptual Consistency evaluationMethodDescription: Not applicable result: (missing) lineage: (LI_Lineage) statement: (missing) processStep: (LI_ProcessStep) description: Data for the NOAA Post Hurricane Sandy Topobathymetric LiDAR Mapping for Shoreline Mapping project was acquired by Quantum Spatial (QS) using three Riegl VQ-820G Topobathy LiDAR systems. All delivered LiDAR data is referenced to: Horizontal Datum-NAD83 (2011) epoch: 2010 Projection-UTM Zone 18 Horizontal Units-meters Vertical Datum-NAD83 (2011) epoch: 2010 (ellipsoid heights) Vertical Units-meters This dataset encompasses 41,388 500m x 500m tiles covering 2,775 square miles along the Atlantic Coast from South Carolina to New York. Green LiDAR data was acquired with the Riegl sensors 9999609, 2220530, and 2220409 and NIR LiDAR data (for water surface model creation that is used during refraction of the green bathymetric data) was acquired with the Leica ALS 50-II sensors 93 and 94 and the Riegl 480 sensor 64.. QS reviewed all acquired flight lines to ensure complete coverage and positional accuracy of the laser points. To correct the continuous onboard measurements of the aircraft position recorded throughout the missions, QS concurrently conducted multiple static Global Navigation Satellite System (GNSS) ground surveys (1 Hz recording frequency) over each monument. After the airborne survey, the static GPS data were triangulated with nearby Continuously Operating Reference Stations (CORS) using the Online Positioning User Service (OPUS) for precise positioning. Multiple independent sessions over the same monument were processed to confirm antenna height measurements and to refine position accuracy. QS then resolved kinematic corrections for aircraft position data using kinematic aircraft GPS and static ground GPS data. A smoothed best estimate trajectory (SBET) was developed that blends post-processed aircraft position with attitude data. Sensor head position and attitude are calculated throughout the survey. The SBET data are used extensively for laser point processing. The software Trimble Business Center v.3.10, Blue Marble Geographic Calculator 2013, and PosPac MMS 6.2 SP2 are used for these processes. Next, QS used RiProcess 1.6 to calculate laser point positioning of the Riegl VQ-820G data by associating SBET positions to each laser point return time, scan angle, intensity, etc. A raw laser point cloud is created in Riegl data format. Erroneous points are filtered and then automated line-to-line calibrations are performed for system attitude parameters (pitch, roll, heading), mirror flex (scale) and GPS/IMU drift. Calibrations are calculated on matching surfaces within and between each line and results are applied to all points in a flight line. Every flight line is used for relative accuracy calibration. This same process is performed on the NIR data using IPAS TC 3.1/Inertial Explorer 8.5 to generate the SBET and Leica ALSPP 2.75 to apply the SBET to the raw scan range files. Green data and NIR data are calibrated together using TerraScan, TerraModeler, and TerraMatch. Accuracy of the calibrated data is assessed using ground RTK survey data. All data are then exported to LAS 1.2 format and are ready for processing and editing. dateTime: DateTime: 2015-05-01T00:00:00 processStep: (LI_ProcessStep) description: QS also creates an initial product call Quick Look Coverage Maps. These Quick Looks files are not fully processed data or final products. The collected LiDAR data is immediately processed in the field by QS to a level that will allow QA\QC measures to determine if the sensor is functioning properly and assess the coverage of submerged topography. An initial SBET is created in POSPAC MMS and used in RiProcess which applies pre-calibrated angular misalignment corrections of scanner position to extract the raw point cloud into geo-referenced LAS files. These files are inspected for sensor malfunctions and then passed through automated classification routines (TerraScan) to develop an initial topo-bathymetric ground model. The ground models are posted to the Sandy project portal where they are further inspected by NOAA to determine adequate coverage of submerged topography for each flight mission of collected LiDAR data. QS and Dewberry both verified relative accuracy on the blocks each contractor was responsible for manually editing. Relative accuracy of the green swaths compared to overlapping and adjacent green swaths as well as the relative accuracy of green swaths compared to overlapping and adjacent NIR swaths was verified through the use Delta-Z (DZ) orthos created in GeoCue software. Dewberry and QS used E-Cognition to create 2D breaklines representing land/water interfaces. These 2D breaklines were manually reviewed and adjusted where necessary to ensure all well-defined hydrographic features (at 1:1200-scale) were represented with breaklines. Using TerraScan, all green LiDAR data within breaklines are classified as water column and a sub-set of these points meeting specific criteria are classified as green water surface points. Using TerraScan, all NIR LiDAR data within breaklines are classified as water column and a sub-set of these points meeting specific criteria are classified as NIR water surface points. Dewberry and QS used the green water surface points and NIR water surface points to create water surface models. These models are used in the refraction tool to determine the depth of bathymetric points and are created for single swaths to ensure temporal differences and wave or water surface height variations between flight lines do not impact the refraction of the bathymetric data. Using the SBET data and the water surface models, all green LiDAR data classified as water column (data within the breaklines) is refracted using Dewberry's LiDAR Processor (DLP). Light travels at different speeds in air versus water and its direction of travel or angle is changed or refracted when entering the water column. The refraction tool corrects for this difference by adjusting the depth (distance traveled) and horizontal position (change of angle/direction) of the green LiDAR data. Using statistics and limited manual review, the output data is verified to ensure the refraction tool functioned properly. Once all green data has been refracted by flight lines, all flight lines covering each tile are combined into a single 500 m x 500 m tile. As the various flight lines may include data collected at Mean Lower Low Water (MLLW) and higher water (HW), which includes everything that is outside the range of MLLW, any HW refracted data points landward of the MLLW land/water interface were classified to class 18 to ensure these HW bathymetric points were not used when MLLW exposed ground points exist in those locations. Dewberry and QS used algoritms in TerraScan to create the intial ground/submerged topography surface. Dewberry then performed manual editing to review and improve the final topobathy surface. Locations of temporal differences were resolved using the Temporal Difference Decision Tree approved by NOAA. Polygons marking the locations of large temporal differences are provided as part of the deliverables. dateTime: DateTime: 2015-05-01T00:00:00 processStep: (LI_ProcessStep) description: All LiDAR data was peer-reviewed. Dewberry used GeoCue software to update LAS header information and QS used LasMonkey to update LAS header information. These updates include updating all projection and coordinate reference system information. The final LiDAR data are in LAS format 1.2 and point data record format 3. The final classificaton scheme is as follows: 1-Unclassified 2-Ground 7-Topo Noise 18-Refracted High Water data landward of the MLLW land/water interface 22-Bathy Noise 23-Sensor Noise (as defined by the sensor using Riegl's noise classifier) 24-Refracted Sensor Noise 25-Water Column 26-Bathymetric Bottom or Submerged Topography 27-Water Surface 30-International Hydrograpic Organization (IHO) S-57 objects 31-Temporal Bathymetric Bottom Dewberry and QS then produced the final set of DZ orthos using the final ground (2) and submerged topography (26) classes. All data is then verified by an Independent QC department within Dewberry. The independent QC is performed by separate analysts who do not perform manual classification or editing. The independent QC involves quantitative and qualitative reviews. dateTime: DateTime: 2015-05-01T00:00:00 processStep: (LI_ProcessStep) description: Dewberry made a copy of the final LiDAR data and transformed the ellipsoid heights into orthometric heights referenced to NAVD88 using Geoid 12A. LiDAR data classified as ground (2) and submerged topography (26) were then converted to ESRI multipoint format. These multipoints were then used to generate a terrain and the terrain was converted to a raster in IMG format with 1 meter pixel resolution. The terrain and output rasters are created over large areas to reduce edge-matching issues and improve seamlessness. The block rasters are clipped to the tile grid and named according to project specifications to result in tiled topobathymetric DEMs. All DEM deliverables will include tiled interpolated DEMs where no void layer is used and the DEMs represent a continuous surface. All DEM deliverables will also include tiled DEMs that incorporate the use of a void layer. Interpolated DEM dataset-These DEMs represent a continuous surface with all void areas interpolated. No void layer was incorporated into this DEM and there are no areas of No Data, regardless of whether the LiDAR data fully penetrated to the submerged topography. Void DEM dataset- The void layer was created in Global Mapper where every bathy bottom point was used to create a grid. The distance or threshold that sets how far Global Mapper can interpolate around each bathy bottom point was set as 2. The higher the interpolation threshold, the more bathy bottom points are connected to create a continuous surface in the Global Mapper grid with fewer areas of NoData. The NoData areas in the Global Mapper grids are exported to polygons. Void polygons greater than 9 square meters are imported into Arc 10.1 Geodatabases where they are incorporated into the terrains as erase features. When the terrains are exported to raster, the void polygons used as an erase in the terrain remain as areas of NoData. A point density layer has been created and provided to NOAA as part of the deliverables. The point density layer is a raster product in IMG format with 1 meter square pixels. The density grid identifies the number of ground and/or bathy bottom points located within each pixel. The pixels in the point density layer align with the pixels in the topobathy DEMs so that the point density layer shows the density of ground/submerged topography points located in each cell that were used to determine elevations for each cell in the topobathy DEMs. Higher density lends itself to higher confidence. The point density layer can be displayed by unique values or classified into desired bins/ranges for analysis over larger areas. A confidence layer has been created and provided to NOAA as part of the deliverables. The confidence layer is a raster product in IMG format with 1 meter square pixels. The confidence layer provides a standard deviation value for every pixel by calculating the standard deviation of all ground and/or submerged topography LiDAR points that are located within a single pixel. The confidence layer pixels align to the pixels in the topobathy DEMs. The confidence layer can be displayed by unique values or classified into desired bins/ranges for analysis over larger areas. dateTime: DateTime: 2015-10-01T00:00:00 processStep: (LI_ProcessStep) description: Data were received by NOAA OCM from NOAA NGS on hard drive in imagine format. OCM mosaiced the 500m x 500m tiles into larger blocks using the gdalwarp version 2.0 program from gdal.org. Blocks match the original 140 block scheme used in the data collection. dateTime: DateTime: 2015-10-01T00:00:00 | |